In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees, In: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights. This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that nevertheless is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime.
- Research Organization:
- Univ. of California, Oakland, CA (United States); Lockheed Martin Corporation, Littleton, CO (United States); UT-Battelle LLC/ORNL, Oak Ridge, TN (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Univ. of Utah, Salt Lake City, UT (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-05CH11231; AC04-94AL85000; AC05-00OR22725; AC52-07NA27344; NA0002375; SC0007446
- OSTI ID:
- 1567374
- Journal Information:
- SC14: INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, Conference: Supercomputing Conference, New Orleans, LA, November 16-21, 2014
- Country of Publication:
- United States
- Language:
- English
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